Analyzing the Interestingness of Association Rules from the Temporal Dimension

@inproceedings{Liu2001AnalyzingTI,
  title={Analyzing the Interestingness of Association Rules from the Temporal Dimension},
  author={Bing Liu and Yiming Ma and Ronnie Lee},
  booktitle={ICDM},
  year={2001}
}
Rule discovery is one of the central tasks of data mining. Existing research has produced many algorithms for the purpose. These algorithms, however, often generate too many rules. In the past few years, rule interestingness techniques were proposed to help the user find interesting rules. These techniques typically employ the dataset as a whole to mine rules, and then filter and/or rank the discovered rules in various ways. In this paper, we argue that this is insufficient. These techniques… CONTINUE READING
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